/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/grappler/optimizers/loop_optimizer.h"

#include <algorithm>
#include <deque>
#include <limits>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "absl/container/flat_hash_set.h"
#include "absl/strings/string_view.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/grappler/graph_topology_view.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/mutable_graph_view.h"
#include "tensorflow/core/grappler/op_types.h"
#include "tensorflow/core/grappler/optimizers/constant_folding.h"
#include "tensorflow/core/grappler/optimizers/evaluation_utils.h"
#include "tensorflow/core/grappler/utils/frame.h"
#include "tensorflow/core/grappler/utils/traversal.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/tensor_coding.h"
#include "tensorflow/core/public/version.h"
#include "tensorflow/core/util/device_name_utils.h"
#include "tensorflow/core/util/saved_tensor_slice_util.h"

using tensorflow::strings::StrCat;

namespace tensorflow {
namespace grappler {
namespace {

using TensorVector = gtl::InlinedVector<TensorValue, 4>;

class LoopInvariantNodeMotionOptimizer {
 public:
  explicit LoopInvariantNodeMotionOptimizer(GraphDef* optimized_graph)
      : optimized_graph_(optimized_graph) {}
  virtual ~LoopInvariantNodeMotionOptimizer() = default;
  Status Optimize();

 private:
  Status FindInvariantNodes(NodeDef* node);
  Status RevertInvariantNodes();
  Status MoveInvariantNodes(const int frame_id);
  Status HandleInvariantNode(NodeDef* node, const int num_outputs,
                             const int frame_id);
  Status HandleConst(NodeDef* node, const int num_outputs, const int frame_id);
  Status HandleInvariantEnter(NodeDef* node, const int num_outputs);

  GraphDef* optimized_graph_;  // Not owned.
  std::unique_ptr<NodeMap> node_map_;
  std::map<NodeDef*, int> invariant_nodes_;
  std::set<int> empty_set_;
  std::vector<std::set<int>> frame_children_;
  std::vector<int> frame_parent_;
  std::map<int, const NodeDef*> loop_cond_;
  std::map<int, std::vector<NodeDef*>> invariant_enters_;
  int new_enter_id_;
};

Status LoopInvariantNodeMotionOptimizer::HandleInvariantEnter(
    NodeDef* node, const int num_outputs) {
  auto consumers = node_map_->GetOutputs(node->name());
  std::vector<string> enter_control_inputs;
  string enter_input;
  for (auto& input : node->input()) {
    if (IsControlInput(input)) {
      enter_control_inputs.push_back(input);
    } else {
      enter_input = input;
    }
  }
  for (auto* consumer : consumers) {
    if (invariant_nodes_.count(consumer)) {
      for (int i = 0; i < consumer->input_size(); ++i) {
        if (NodeName(consumer->input(i)) == node->name()) {
          consumer->set_input(i, enter_input);
          node_map_->AddOutput(NodeName(enter_input), consumer->name());
          node_map_->RemoveOutput(node->name(), consumer->name());
        }
      }
      for (auto& control_input : enter_control_inputs) {
        consumer->add_input(control_input);
        node_map_->AddOutput(NodeName(control_input), consumer->name());
      }
    }
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::HandleConst(NodeDef* node,
                                                     const int num_outputs,
                                                     const int frame_id) {
  NodeDef* const_node = nullptr;
  if (num_outputs == 0) {
    // all successor nodes are invariant
    // Remove the control inputs from this frame to the const node,
    // when moving it out of the frame (in parent frame)
    const_node = node;
    node_map_->RemoveInputs(node->name());
    node->clear_input();
  } else {
    // some successor nodes are variant
    // Have to keep the const node in the frame,
    // so create a new one outside the frame (in parent frame)
    const string const_node_name =
        AddPrefixToNodeName(node->name(), kLoopOptimizer);
    const_node = node_map_->GetNode(const_node_name);
    if (const_node == nullptr) {
      const_node = optimized_graph_->add_node();
      const_node->set_name(const_node_name);
      const_node->set_op("Const");
      const_node->set_device(node->device());
      *const_node->mutable_attr() = node->attr();
      node_map_->AddNode(const_node->name(), const_node);
    }
    auto consumers = node_map_->GetOutputs(node->name());
    for (auto* consumer : consumers) {
      if (invariant_nodes_.count(consumer)) {
        for (int i = 0; i < consumer->input_size(); ++i) {
          if (NodeName(consumer->input(i)) == node->name()) {
            if (IsControlInput(consumer->input(i))) {
              *consumer->mutable_input(i) = AsControlDependency(*const_node);
            } else {
              *consumer->mutable_input(i) = const_node->name();
            }
            node_map_->AddOutput(const_node->name(), consumer->name());
            node_map_->RemoveOutput(node->name(), consumer->name());
          }
        }
      }
    }
  }
  // add a control input from the parent frame
  if (frame_parent_[frame_id] != -1) {
    int parent_id = frame_parent_[frame_id];
    auto loop_cond_it = loop_cond_.find(parent_id);
    if (loop_cond_it == loop_cond_.end()) {
      return errors::InvalidArgument("Frame ", frame_id,
                                     " doesn't have a LoopCond node");
    }
    auto& loop_cond_name = loop_cond_it->second->name();
    NodeDef* switch_node = nullptr;
    for (auto* node : node_map_->GetOutputs(loop_cond_name)) {
      if (node->op() == "Switch") {
        switch_node = node;
        break;
      }
    }
    if (!switch_node) {
      return errors::InvalidArgument("LoopCond node of Frame ", frame_id,
                                     " doesn't connect to any Switch node");
    }
    string switch_output = StrCat(switch_node->name(), ":1");
    const string ctrl_dep = ConstantFolding::AddControlDependency(
        switch_output, optimized_graph_, node_map_.get());
    const_node->add_input(ctrl_dep);
    node_map_->AddOutput(NodeName(ctrl_dep), const_node->name());
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::HandleInvariantNode(
    NodeDef* node, const int num_outputs, const int frame_id) {
  // have to remove control inputs to the invariant node from the same frame
  // when moving this node out of this frame
  for (int i = 0; i < node->input_size(); ++i) {
    if (IsControlInput(node->input(i))) {
      node->mutable_input()->SwapElements(i, node->input_size() - 1);
      node->mutable_input()->RemoveLast();
    }
  }
  if (num_outputs == 0) {
    return OkStatus();
  }

  DataTypeVector input_types;
  DataTypeVector output_types;
  OpRegistryInterface* op_registry = OpRegistry::Global();
  const OpRegistrationData* op_reg_data = nullptr;
  TF_RETURN_IF_ERROR(op_registry->LookUp(node->op(), &op_reg_data));
  TF_RETURN_IF_ERROR(InOutTypesForNode(*node, op_reg_data->op_def, &input_types,
                                       &output_types));

  auto consumers = node_map_->GetOutputs(node->name());
  string fname = invariant_enters_[frame_id][0]->attr().at("frame_name").s();
  int piterations =
      invariant_enters_[frame_id][0]->attr().at("parallel_iterations").i();
  for (auto* consumer : consumers) {
    if (!invariant_nodes_.count(consumer)) {
      for (int i = 0; i < consumer->input_size(); ++i) {
        int port;
        string node_name = ParseNodeName(consumer->input(i), &port);
        if (node_name != node->name()) {
          continue;
        }
        if (port < 0) {
          return errors::InvalidArgument(
              "Invariant node should not have control outputs "
              "to variant node");
        }
        DataType output_type = output_types[port];
        NodeDef* new_enter = optimized_graph_->add_node();
        new_enter->set_op("Enter");
        new_enter->set_device(node->device());
        new_enter->set_name(AddPrefixToNodeName(
            StrCat(fname, "_enter_", new_enter_id_++), kLoopOptimizer));
        AttrValue data_type;
        data_type.set_type(output_type);
        new_enter->mutable_attr()->insert({"T", data_type});
        AttrValue frame_name;
        frame_name.set_s(fname);
        new_enter->mutable_attr()->insert({"frame_name", frame_name});
        AttrValue is_const;
        is_const.set_b(true);
        new_enter->mutable_attr()->insert({"is_constant", is_const});
        AttrValue parallel_iterations;
        parallel_iterations.set_i(piterations);
        new_enter->mutable_attr()->insert(
            {"parallel_iterations", parallel_iterations});
        new_enter->add_input(consumer->input(i));
        *consumer->mutable_input(i) = new_enter->name();
        node_map_->AddNode(new_enter->name(), new_enter);
        node_map_->AddOutput(node->name(), new_enter->name());
        node_map_->AddOutput(new_enter->name(), consumer->name());
      }
    }
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::MoveInvariantNodes(
    const int frame_id) {
  for (auto iter = invariant_nodes_.begin(); iter != invariant_nodes_.end();
       ++iter) {
    auto* invariant_node = iter->first;
    const int num_outputs = iter->second;
    if (IsEnter(*invariant_node)) {
      TF_RETURN_IF_ERROR(HandleInvariantEnter(invariant_node, num_outputs));
    } else if (IsConstant(*invariant_node)) {
      TF_RETURN_IF_ERROR(HandleConst(invariant_node, num_outputs, frame_id));
    } else {
      TF_RETURN_IF_ERROR(
          HandleInvariantNode(invariant_node, num_outputs, frame_id));
    }
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::RevertInvariantNodes() {
  std::deque<const NodeDef*> reverted_nodes;
  for (auto iter = invariant_nodes_.begin(); iter != invariant_nodes_.end();) {
    bool erased = false;
    const auto* node = iter->first;
    if (!IsConstant(*node) && !IsEnter(*node) && iter->second > 0) {
      auto& consumers = node_map_->GetOutputs(node->name());
      for (auto* consumer : consumers) {
        if (!invariant_nodes_.count(consumer)) {
          for (const auto& input : consumer->input()) {
            if (IsControlInput(input) && NodeName(input) == node->name()) {
              reverted_nodes.push_back(node);
              invariant_nodes_.erase(iter++);
              erased = true;
              break;
            }
          }
          if (erased) break;
        }
      }
    }
    if (!erased) ++iter;
  }
  while (!reverted_nodes.empty()) {
    const auto* node = reverted_nodes.front();
    reverted_nodes.pop_front();
    std::set<NodeDef*> producers;
    for (const auto& input : node->input()) {
      auto* producer = node_map_->GetNode(input);
      auto iter = invariant_nodes_.find(producer);
      if (iter != invariant_nodes_.end()) {
        if (IsControlInput(input) && !IsConstant(*producer) &&
            !IsEnter(*producer)) {
          reverted_nodes.push_back(producer);
          invariant_nodes_.erase(iter);
        } else {
          producers.insert(producer);
        }
      }
    }
    for (auto* producer : producers) {
      auto iter = invariant_nodes_.find(producer);
      if (iter != invariant_nodes_.end()) {
        ++iter->second;
      }
    }
    for (auto* consumer : node_map_->GetOutputs(node->name())) {
      auto iter = invariant_nodes_.find(consumer);
      if (iter != invariant_nodes_.end()) {
        reverted_nodes.push_back(consumer);
        invariant_nodes_.erase(iter);
      }
    }
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::FindInvariantNodes(
    NodeDef* start_node) {
  std::vector<NodeDef*> stack;
  stack.reserve(32);
  stack.push_back(start_node);
  while (!stack.empty()) {
    NodeDef* node = stack.back();
    stack.pop_back();
    auto consumers = node_map_->GetOutputs(node->name());
    invariant_nodes_.emplace(node, consumers.size());
    for (auto* consumer : consumers) {
      if (invariant_nodes_.count(consumer) || ModifiesFrameInfo(*consumer)) {
        continue;
      }
      bool is_invariant = true;
      for (const auto& input : consumer->input()) {
        if (!IsControlInput(input)) {
          const string name = NodeName(input);
          auto* producer = node_map_->GetNode(name);
          if (!invariant_nodes_.count(producer)) {
            if (IsConstant(*producer)) {
              invariant_nodes_.insert(
                  std::make_pair(producer, node_map_->GetOutputs(name).size()));
            } else {
              is_invariant = false;
              break;
            }
          }
        }
      }
      if (is_invariant) {
        std::set<NodeDef*> producers;
        for (const auto& input : consumer->input()) {
          auto* producer = node_map_->GetNode(input);
          producers.insert(producer);
        }
        for (auto* producer : producers) {
          auto iter = invariant_nodes_.find(producer);
          if (iter != invariant_nodes_.end()) {
            --iter->second;
          }
        }
        stack.push_back(consumer);
      }
    }
  }
  return OkStatus();
}

Status LoopInvariantNodeMotionOptimizer::Optimize() {
  node_map_.reset(new NodeMap(optimized_graph_));
  FrameView frame_view;
  // TODO(ezhulenev): Use GraphView when migrated from NodeMap.
  TF_RETURN_IF_ERROR(frame_view.InferFromGraph(*optimized_graph_));

  frame_parent_.resize(frame_view.num_frames(), -1);
  frame_children_.resize(frame_view.num_frames());
  std::deque<int> worklist;
  for (const NodeDef& node : optimized_graph_->node()) {
    const std::vector<int>& frame_ids = frame_view.Frames(node);

    if (frame_ids.size() >= 3) {
      for (unsigned int i = 1; i < frame_ids.size() - 1; ++i) {
        frame_parent_[frame_ids[i]] = frame_ids[i - 1];
        frame_children_[frame_ids[i]].insert(frame_ids[i + 1]);
      }
    }
    if (frame_ids.size() >= 2) {
      frame_children_[frame_ids[0]].insert(frame_ids[1]);
      frame_parent_[frame_ids.back()] = frame_ids[frame_ids.size() - 2];
    }
    if (!frame_ids.empty()) {
      frame_children_[frame_ids.back()] = empty_set_;
      if (node.op() == "LoopCond") {
        if (loop_cond_.count(frame_ids.back())) {
          return errors::InvalidArgument(
              "Loop ", frame_ids.back(),
              " has more than one LoopCond node: ", node.name(), " and ",
              loop_cond_[frame_ids.back()]->name());
        }
        loop_cond_[frame_ids.back()] = &node;
      }
      if (IsEnter(node) && node.attr().at("is_constant").b()) {
        invariant_enters_[frame_ids.back()].push_back(
            const_cast<NodeDef*>(&node));
      }
    }
  }

  for (size_t i = 0; i < frame_children_.size(); i++) {
    if (frame_children_[i].empty()) {
      worklist.push_back(i);
    }
  }

  while (!worklist.empty()) {
    int frame_id = worklist.front();
    new_enter_id_ = 0;
    worklist.pop_front();
    if (frame_parent_[frame_id] != -1) {
      int parent_id = frame_parent_[frame_id];
      frame_children_[parent_id].erase(frame_id);
      if (frame_children_[parent_id].empty()) {
        worklist.push_back(parent_id);
      }
    }

    if (invariant_enters_[frame_id].empty()) {
      continue;
    }
    invariant_nodes_.clear();
    for (auto* enter : invariant_enters_[frame_id]) {
      TF_RETURN_IF_ERROR(FindInvariantNodes(enter));
    }

    // revert invariant nodes that have control outputs to variant nodes
    TF_RETURN_IF_ERROR(RevertInvariantNodes());

    TF_RETURN_IF_ERROR(MoveInvariantNodes(frame_id));
  }
  return OkStatus();
}

std::vector<int> GetStackPushNodesToConvert(
    const GraphTopologyView& graph_view,
    const std::unordered_set<string>& nodes_to_preserve, int stack_node_idx) {
  VLOG(1) << "Stack node: " << graph_view.graph()->node(stack_node_idx).name();

  const std::unordered_set<string> op_types_to_traverse(
      {"Stack", "StackV2", "Enter", "RefEnter", "Switch", "RefSwitch",
       "_SwitchN", "Identity", "RefIdentity"});
  const auto is_op_to_traverse = [&](const NodeDef* node) -> bool {
    return op_types_to_traverse.find(node->op()) != op_types_to_traverse.end();
  };

  std::vector<int> nodes_to_convert;
  std::vector<int> fanouts;

  DfsTraversal(graph_view, {graph_view.GetNode(stack_node_idx)},
               TraversalDirection::kFollowOutputs,
               DfsPredicates::Advance(is_op_to_traverse),
               DfsCallbacks::PreOrder([&](const NodeDef* node) {
                 const absl::optional<int> idx = graph_view.GetNodeIndex(*node);
                 fanouts.push_back(idx.value());
               }));

  for (int fanout_idx : fanouts) {
    const NodeDef& fanout_node = graph_view.graph()->node(fanout_idx);
    VLOG(1) << "Fanout " << fanout_idx << " : " << fanout_node.name();
    if (IsStackPushOp(fanout_node)) {
      // Check that the stack itself is not a node we want to preserve. This can
      // happen when the graph we have contains only the forward pass for a loop
      // (as when the forward and backward passes are split across different
      // functions).
      if (graph_view.HasNode(fanout_node.input(0))) {
        const NodeDef* stack_node = graph_view.GetNode(fanout_node.input(0));
        while (stack_node->op() != "Stack" && stack_node->op() != "StackV2" &&
               stack_node->input_size() > 0 &&
               graph_view.HasNode(stack_node->input(0))) {
          stack_node = graph_view.GetNode(stack_node->input(0));
        }
        if (nodes_to_preserve.find(stack_node->name()) ==
            nodes_to_preserve.end()) {
          nodes_to_convert.push_back(fanout_idx);
        }
      } else {
        nodes_to_convert.push_back(fanout_idx);
      }
    } else if (IsStackOp(fanout_node) || IsStackCloseOp(fanout_node) ||
               op_types_to_traverse.find(fanout_node.op()) !=
                   op_types_to_traverse.end()) {
      continue;
    } else if (!IsStackPopOp(fanout_node) ||
               (!graph_view.GetFanout(fanout_idx).empty() ||
                nodes_to_preserve.find(fanout_node.name()) !=
                    nodes_to_preserve.end())) {
      // The node is either a stack pop with consumers or something unexpected
      // so we leave the graph alone.
      nodes_to_convert.clear();
      break;
    }
  }

  return nodes_to_convert;
}

Status RemoveStackOps(const std::unordered_set<string>& nodes_to_preserve,
                      GraphDef* optimized_graph) {
  NodeMap node_map(optimized_graph);
  GraphTopologyView graph_view;
  TF_RETURN_IF_ERROR(graph_view.InitializeFromGraph(*optimized_graph));

  for (int node_idx = 0; node_idx < optimized_graph->node_size(); ++node_idx) {
    if (IsStackOp(optimized_graph->node(node_idx))) {
      for (int push_node_idx : GetStackPushNodesToConvert(
               graph_view, nodes_to_preserve, node_idx)) {
        // We found push nodes without corresponding pops. Convert them to
        // Identity passing the data through and add a control dependency from
        // the op supplying the stack handle.
        NodeDef* push_node = optimized_graph->mutable_node(push_node_idx);
        VLOG(1) << "Converting " << push_node_idx << " : "
                << push_node->DebugString();
        if (push_node->attr().count("swap_memory") != 0) {
          push_node->mutable_attr()->erase("swap_memory");
        }
        push_node->set_op("Identity");
        push_node->mutable_input()->SwapElements(0, 1);
        const string ctrl_dep = ConstantFolding::AddControlDependency(
            push_node->input(1), optimized_graph, &node_map);
        push_node->set_input(1, ctrl_dep);
        VLOG(1) << "After converting: " << push_node->DebugString();
      }
    }
  }
  return OkStatus();
}

bool IsSimpleBinaryOperator(const NodeDef& node) {
  return (IsLess(node) || IsLessEqual(node) || IsGreater(node) ||
          IsGreaterEqual(node) || IsEqual(node));
}

Status EvaluateBoolOpForConstantOperands(const NodeDef& op_node,
                                         const NodeDef& constant_operand_0,
                                         const NodeDef& constant_operand_1,
                                         DeviceBase* cpu_device,
                                         ResourceMgr* resource_mgr,
                                         bool* value) {
  VLOG(4) << "Evaluate bool op: op_node=" << op_node.name()
          << " input0=" << constant_operand_0.name()
          << " input1=" << constant_operand_1.name();
  TensorVector inputs;

  const TensorProto& raw_val_0 = constant_operand_0.attr().at("value").tensor();
  Tensor value_0(raw_val_0.dtype(), raw_val_0.tensor_shape());
  CHECK(value_0.FromProto(raw_val_0));
  inputs.emplace_back(&value_0);
  const TensorProto& raw_val_1 = constant_operand_1.attr().at("value").tensor();
  Tensor value_1(raw_val_1.dtype(), raw_val_1.tensor_shape());
  CHECK(value_1.FromProto(raw_val_1));
  inputs.emplace_back(&value_1);

  TensorVector outputs;
  TF_RETURN_IF_ERROR(
      EvaluateNode(op_node, inputs, cpu_device, resource_mgr, &outputs));

  if (outputs.size() != 1 || outputs[0].tensor == nullptr) {
    return Status(absl::StatusCode::kInvalidArgument, "Expected one output.");
  }
  *value = outputs[0].tensor->scalar<bool>()();
  delete outputs[0].tensor;

  return OkStatus();
}

// TODO(lyandy): Consolidate with ConstantFolding implementation.
bool IsReallyConstant(const NodeDef& node,
                      const absl::flat_hash_set<string>& feed_nodes) {
  if (!IsConstant(node)) {
    return false;
  }
  // If the node is fed it's not constant anymore.
  return feed_nodes.find(node.name()) == feed_nodes.end();
}

Status CheckForDeadFanout(const MutableGraphView& view,
                          const NodeDef& switch_node, const NodeMap& node_map,
                          const absl::flat_hash_set<string>& feed_nodes,
                          DeviceBase* cpu_device, ResourceMgr* resource_mgr,
                          bool* has_dead_fanout, int* dead_fanout) {
  *has_dead_fanout = false;
  GraphView::InputPort switch_loopcond_port(&switch_node, 1);
  const NodeDef* switch_predicate =
      view.GetRegularFanin(switch_loopcond_port).node;

  // CASE 1: Control is a constant.
  if (IsReallyConstant(*switch_predicate, feed_nodes)) {
    VLOG(3) << "Found switch node with constant predicate:"
            << " switch_node=" << switch_node.name()
            << " switch_predicate=" << switch_predicate->name();
    Tensor selector;
    CHECK(selector.FromProto(switch_predicate->attr().at("value").tensor()));
    *has_dead_fanout = true;
    *dead_fanout = selector.scalar<bool>()() ? 0 : 1;
    return OkStatus();
  }

  GraphView::InputPort switch_input_port(&switch_node, 0);
  const NodeDef* switch_input = view.GetRegularFanin(switch_input_port).node;

  // CASE 2: Zero-iteration while loop.
  // We check if its a while loop such that the condition is a simple binary
  // operator which returns false for the initialization value.
  // TODO(srjoglekar): Improve to work with arbitrary predicate subgraphs.
  if (!IsMerge(*switch_input) || !IsLoopCond(*switch_predicate)) {
    return OkStatus();
  }

  VLOG(4) << "Try to find a zero iteration while loop:"
          << " switch_node=" << switch_node.name();

  // Find the boolean predicate from a LoopCond node (e.g. Greater).
  NodeDef* switch_ctrl_node = view.GetRegularFanin({switch_predicate, 0}).node;
  if (!switch_ctrl_node || !IsSimpleBinaryOperator(*switch_ctrl_node)) {
    return OkStatus();
  }

  // Find the Merge node & the Constant Operand to the condition node, if
  // available.
  NodeDef* merge_node = nullptr;
  NodeDef* constant_ctrl_input = nullptr;
  int constant_index = 0;
  for (int i = 0; i < switch_ctrl_node->input().size(); ++i) {
    const string& input = switch_ctrl_node->input(i);
    if (IsControlInput(input)) continue;

    NodeDef* node = view.GetNode(switch_ctrl_node->input(i));
    if (IsMerge(*node)) {
      merge_node = node;
    }
    if (IsReallyConstant(*node, feed_nodes)) {
      constant_ctrl_input = node;
      constant_index = i;
    }
  }
  if (merge_node == nullptr || constant_ctrl_input == nullptr) {
    return OkStatus();
  }

  // Find the initialization constant (via Enter, if one exists).
  NodeDef* enter_node = nullptr;
  NodeDef* constant_init_node = nullptr;
  for (const auto& input : merge_node->input()) {
    NodeDef* node = node_map.GetNode(input);
    if (IsEnter(*node)) {
      enter_node = node;
    }
    if (IsReallyConstant(*node, feed_nodes)) {
      constant_init_node = node;
    }
  }
  if (enter_node != nullptr) {
    if (constant_init_node != nullptr) return OkStatus();
    for (const auto& input : enter_node->input()) {
      NodeDef* node = node_map.GetNode(input);
      if (IsReallyConstant(*node, feed_nodes)) {
        constant_init_node = node;
      }
    }
  }
  if (constant_init_node == nullptr) {
    return OkStatus();
  }

  VLOG(4) << "Check if loop will be 0 iterations:"
          << "\n|  switch_node        : " << switch_node.name()
          << "\n|  switch_ctrl_node   : " << switch_ctrl_node->name()
          << "\n|  merge_node         : " << merge_node->name()
          << "\n|  constant_ctrl_input: " << constant_ctrl_input->name()
          << "\n|  enter_node         : "
          << (enter_node ? enter_node->name() : "<n/a>")
          << "\n|  constant_init_node : " << constant_init_node->name();

  // Check if there will be 0 iterations. This will only happen if the condition
  // evaluates to false with respect to the initialization value.
  NodeDef* operand_0 =
      constant_index ? constant_init_node : constant_ctrl_input;
  NodeDef* operand_1 =
      constant_index ? constant_ctrl_input : constant_init_node;
  bool constant_switch_value;
  TF_RETURN_IF_ERROR(EvaluateBoolOpForConstantOperands(
      *switch_ctrl_node, *operand_0, *operand_1, cpu_device, resource_mgr,
      &constant_switch_value));

  if (constant_switch_value == false) {
    VLOG(3) << "Remove 0 iteration while loop:"
            << " switch_node=" << switch_node.name();
    *has_dead_fanout = true;
    *dead_fanout = 1;
  } else {
    VLOG(4) << "Was not able to prove that loop has 0 iterations.";
  }
  return OkStatus();
}

}  // namespace

LoopOptimizer::LoopOptimizer()
    : opt_level_(RewriterConfig::ON),
      cpu_device_(nullptr),
      options_(LoopOptimizerOptions::Default(RewriterConfig::ON)) {}

LoopOptimizer::LoopOptimizer(RewriterConfig::Toggle opt_level,
                             DeviceBase* cpu_device)
    : opt_level_(opt_level),
      cpu_device_(cpu_device),
      options_(LoopOptimizerOptions::Default(RewriterConfig::ON)) {
  resource_mgr_.reset(new ResourceMgr());
}

Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                               GraphDef* optimized_graph) {
  if (!options_.enable_loop_invariant_node_motion &&
      !options_.enable_stack_push_removal &&
      !options_.enable_dead_branch_removal) {
    return errors::Aborted("Nothing to do.");
  }
  *optimized_graph = item.graph;
  // Set up helper data structures.
  if (options_.enable_loop_invariant_node_motion) {
    LoopInvariantNodeMotionOptimizer linm_optimizer(optimized_graph);
    TF_RETURN_IF_ERROR(linm_optimizer.Optimize());
  }
  if (options_.enable_stack_push_removal) {
    TF_RETURN_IF_ERROR(RemoveStackOps(item.NodesToPreserve(), optimized_graph));
  }
  if (options_.enable_dead_branch_removal) {
    NodeMap node_map(optimized_graph);
    absl::flat_hash_set<string> feed_nodes;
    for (const auto& feed : item.feed) {
      feed_nodes.insert(NodeName(feed.first));
    }
    TF_RETURN_IF_ERROR(RemoveDeadBranches(item.NodesToPreserve(), node_map,
                                          feed_nodes, optimized_graph));
  }

  return OkStatus();
}

// An Identity node has only 1 output, but Switch and Merge nodes have 2.
// Update full type information (which describes the output) if present, i.e.
// do simple type inference.
static Status update_identity_node_type(NodeDef* sw_node) {
  if (sw_node->has_experimental_type() &&
      (sw_node->experimental_type().type_id() == TFT_PRODUCT)) {
    FullTypeDef old_t = sw_node->experimental_type();
    if (old_t.args_size() != 2) {
      return errors::Internal(
          "When converting Switch or Merge node '", sw_node->name(),
          "' to Identity, full type of original node describes ",
          old_t.args_size(), " outputs, not 2.\n", old_t.DebugString());
    }
    FullTypeDef new_t;
    new_t.set_type_id(TFT_PRODUCT);
    // For a Merge node, the type of output 0 matches the output of the
    // corresponding Identity. For a Switch node, the type of both outputs
    // is the same and matches the output of the corresponding identity,
    // so using output 0 is fine.
    *(new_t.add_args()) = old_t.args()[0];
    *(sw_node->mutable_experimental_type()) = new_t;
  }
  return OkStatus();
}

Status LoopOptimizer::RemoveDeadBranches(
    const std::unordered_set<string>& nodes_to_preserve, NodeMap& node_map,
    const absl::flat_hash_set<string>& feed_nodes, GraphDef* optimized_graph) {
  std::unordered_set<const NodeDef*> dead_nodes;
  std::unordered_map<NodeDef*, std::set<int>> dead_merge_inputs;
  absl::flat_hash_set<GraphView::OutputPort> identity_switches;

  MutableGraphView view(optimized_graph);
  for (const NodeDef& node : optimized_graph->node()) {
    if (!IsSwitch(node)) {
      continue;
    }
    if (node.op() == "_SwitchN") {  // _SwitchN not used in loop control flow.
      continue;
    }
    if (nodes_to_preserve.find(node.name()) != nodes_to_preserve.end()) {
      continue;
    }

    int dead_fanout;
    bool has_dead_fanout;
    TF_RETURN_IF_ERROR(CheckForDeadFanout(view, node, node_map, feed_nodes,
                                          cpu_device_, resource_mgr_.get(),
                                          &has_dead_fanout, &dead_fanout));
    if (!has_dead_fanout) {
      continue;
    }
    GraphView::OutputPort dead(&node, dead_fanout);

    SetVector<MutableGraphView::InputPort, absl::Hash<MutableGraphView::Port>>
        zombie_inputs;
    for (const MutableGraphView::InputPort& port : view.GetFanout(dead)) {
      if (dead_nodes.find(port.node) == dead_nodes.end()) {
        zombie_inputs.PushBack(port);
      }
    }
    // If we encounter a single node that must be preserved in the fanout of the
    // switch node we need to preserve the entire switch fanout: we therefore
    // work on a local copy that only gets committed to the master copy once the
    // whole fanout has been explored.
    std::unordered_set<const NodeDef*> local_dead_nodes = dead_nodes;
    std::unordered_map<NodeDef*, std::set<int>> local_dead_merge_inputs =
        dead_merge_inputs;
    bool found_node_to_preserve = false;
    while (!found_node_to_preserve && !zombie_inputs.Empty()) {
      MutableGraphView::InputPort dead = zombie_inputs.PopBack();
      if (nodes_to_preserve.find(dead.node->name()) !=
          nodes_to_preserve.end()) {
        found_node_to_preserve = true;
        break;
      }

      if (local_dead_nodes.find(dead.node) != local_dead_nodes.end()) {
        continue;
      }

      if (IsMerge(*dead.node)) {
        const int num_data_inputs = dead.node->attr().at("N").i();
        if (num_data_inputs > 2) {
          // This can happen with _SwitchN/Merge (Case lowering). We skip these
          // to simplify the code for now.
          found_node_to_preserve = true;
          break;
        }
        MutableGraphView::OutputPort value_index(dead.node, 1);
        const absl::flat_hash_set<MutableGraphView::InputPort>& index_fanout =
            view.GetFanout(value_index);
        if (!index_fanout.empty()) {
          // The 2nd output (that indicates which input is propagated) is
          // connected. This never happens in practice, so we'll just skip this
          // case to simplify the code for now.
          found_node_to_preserve = true;
          break;
        }

        bool fully_dead = false;
        // Merge node can become real dead only if all data inputs are dead.
        // Merge always waits for all control edges, but they do not
        // change the node deadness.
        if (dead.port_id >= 0) {
          local_dead_merge_inputs[dead.node].insert(dead.port_id);
          if (local_dead_merge_inputs[dead.node].size() == num_data_inputs) {
            fully_dead = true;
          }
        } else {
          // Keep track of all Merge nodes, even if they do not have dead data
          // inputs. We'll need to cleanup dead control edges for them later.
          local_dead_merge_inputs.insert({dead.node, {}});
        }
        if (fully_dead) {
          local_dead_merge_inputs.erase(dead.node);
          local_dead_nodes.insert(dead.node);
          for (const MutableGraphView::InputPort& port :
               view.GetFanouts(*dead.node, true)) {
            zombie_inputs.PushBack(port);
          }
        }
      } else if (dead.node->op() == "ControlTrigger") {
        // Control trigger have different semantic, so don't touch them
        found_node_to_preserve = true;
        break;
      } else {
        if (local_dead_nodes.insert(dead.node).second) {
          for (const MutableGraphView::InputPort& dead_fanout :
               view.GetFanouts(*dead.node, true)) {
            zombie_inputs.PushBack(dead_fanout);
          }
        }
      }
    }
    if (!found_node_to_preserve) {
      std::swap(dead_nodes, local_dead_nodes);
      std::swap(dead_merge_inputs, local_dead_merge_inputs);
      // Found no nodes to preserve in fanout of this switch node. This switch
      // node can be replaced with Identity node, collect here to process later
      identity_switches.insert(dead);
      VLOG(3) << "Found no nodes to preserve in fanout of switch node: "
              << node.name() << ", fanout port: " << dead_fanout;
    }
  }

  std::vector<int> nodes_idx_to_delete;
  nodes_idx_to_delete.reserve(dead_nodes.size());
  for (int i = 0; i < optimized_graph->node_size(); ++i) {
    if (dead_nodes.count(&optimized_graph->node(i)))
      nodes_idx_to_delete.push_back(i);
  }

  // Names of the nodes that were removed from the graph.
  absl::flat_hash_set<absl::string_view> dead_node_names;
  dead_node_names.reserve(dead_nodes.size());
  for (const NodeDef* dead_node : dead_nodes) {
    dead_node_names.insert(dead_node->name());
  }

  // Check that the merge nodes are valid.
  for (const auto& itr : dead_merge_inputs) {
    NodeDef* merge_node = itr.first;
    if (dead_nodes.find(merge_node) != dead_nodes.end()) {
      // The node will be pruned since all its inputs are dead.
      continue;
    }
    // Remove dead data input.
    const std::set<int>& dead_inputs = itr.second;
    const int num_data_inputs = merge_node->attr().at("N").i();
    if (merge_node->input_size() != num_data_inputs) {
      LOG(WARNING)
          << "Skipping loop optimization for Merge node with control input: "
          << merge_node->name();
      return OkStatus();
    } else if (dead_inputs.size() != 1 || num_data_inputs != 2) {
      LOG(WARNING) << "Skipping loop optimization for Merge node ("
                   << merge_node->name()
                   << ") with unexpected dead_inputs.size() ("
                   << dead_inputs.size() << " or  num_data_inputs"
                   << num_data_inputs;
      return OkStatus();
    }
  }

  // Remove dead inputs from Merge nodes that will not be not
  // pruned from the graph.
  for (const auto& itr : dead_merge_inputs) {
    NodeDef* merge_node = itr.first;
    if (dead_nodes.find(merge_node) != dead_nodes.end()) {
      // The node will be pruned since all its inputs are dead.
      continue;
    }
    VLOG(3) << "Merge node before cleanup: " << merge_node->DebugString();
    // Remove dead data input.
    const std::set<int>& dead_inputs = itr.second;
    int index = *dead_inputs.begin();
    auto* inputs = merge_node->mutable_input();
    inputs->SwapElements(1, index);
    inputs->SwapElements(1, merge_node->input_size() - 1);
    inputs->RemoveLast();
    merge_node->set_op("Identity");
    merge_node->mutable_attr()->erase("N");
    TF_RETURN_IF_ERROR(update_identity_node_type(merge_node));

    VLOG(3) << "Merge node after cleanup: " << merge_node->DebugString();
  }

  for (const auto& id_switch : identity_switches) {
    NodeDef* sw_node = const_cast<NodeDef*>((id_switch.node));
    int dead_port_id = id_switch.port_id;

    // Switch node where pred is not a constant, is not optimized.
    // TODO(intel-tf): For that case, enable optimization only if safe.
    // TODO(intel-tf): Need to check for RefSwitch and replace RefSwitch with
    // RefIdentity
    NodeDef* pred = node_map.GetNode(sw_node->input(1));
    if (IsReallyConstant(*pred, feed_nodes) && sw_node->op() == "Switch") {
      // From the dead_port_id, get the live port id, so we can correct
      // input names of consumers. When switch will be replaced with Identity,
      // it will have only 1 output versus 2 outputs of a Switch node
      int live_port_id = (dead_port_id + 1) % 2;
      string live_output_name = sw_node->name();
      if (live_port_id == 1) {
        live_output_name = StrCat(sw_node->name(), ":1");
      }

      // Get consumers of live port and update the input names
      auto consumers = node_map.GetOutputs(sw_node->name());
      for (auto* consumer : consumers) {
        for (int i = 0; i < consumer->input_size(); ++i) {
          if (consumer->input(i) == live_output_name) {
            consumer->set_input(i, sw_node->name());
            node_map.UpdateInput(consumer->name(), live_output_name,
                                 sw_node->name());
          }
        }
      }

      VLOG(3) << "Switch node before cleanup: " << sw_node->DebugString();

      // Change node from Switch to Identity and add a control dependency to
      // this Identity op.
      const string ctrl_dep = ConstantFolding::AddControlDependency(
          pred->name(), optimized_graph, &node_map);
      node_map.UpdateInput(sw_node->name(), pred->name(), ctrl_dep);
      sw_node->set_input(1, ctrl_dep);
      sw_node->set_op("Identity");
      TF_RETURN_IF_ERROR(update_identity_node_type(sw_node));
      VLOG(3) << "Switch node after cleanup: " << sw_node->DebugString();
    }
  }
  EraseNodesFromGraph(std::move(nodes_idx_to_delete), optimized_graph);

  return OkStatus();
}

}  // end namespace grappler
}  // end namespace tensorflow
